1. Technical Field
This invention relates to monitoring computer-controlled processes.
2. Description of Related Art
Many processes are controlled by computers. Examples range from technical processes like manufacturing, engine management, etc, through information technology applications such as air traffic control, to business processes such as work scheduling or customer call centre management. Such processes are designed around predetermined assumptions about the environment in which the process is to operate. Should that environment be changed, those assumptions may no longer be valid, and the controlling software may have to be adapted or replaced in order to maintain a required quality of operation. Moreover, whenever the process itself changes, the system that it controls also needs to be adapted to the new requirements. In both cases, it is quite often sufficient to change the process data that controls the system, rather than change the entire system. The process data is typically embodied in software for controlling a computer, the process software itself being installed either as a physical component carrying the necessary programme data, such as a plug-in module or a carrier such as a CD-ROM, or in the form of a signal carried over a communications network.
In currently available systems, changing the process data requires human intervention. New requirements have to be identified, the process data has to be redesigned according to those requirements, and then implemented, tested and finally installed into the system.
Consider a call centre, from which customers are to be contacted in order to offer them new services, or to follow up previous contact with the customers. Typically the process that selects the customers to be contacted is implemented as a piece of software. Amongst many others, typical categories of requirements for such a process are levels of simplicity, adaptability, accuracy and execution time.
More specifically, requirements may include the use of a rule-based process, so that the process manager can understand the selection process that determines which customers are to be contacted. Rule-based processes may be necessary in order to ensure compliance with the rules laid down by regulatory bodies. The number of rules may be constrained in order to keep the process comprehensible.
Another requirement may be adaptability to new data, so that the process can be adapted as new customer or process data becomes available. In particular, time or data storage constraints may make it impossible to use all historic data when recalculating trends and other statistical data.
Another common requirement is a specified level of accuracy; given historic information the process should aim to only contact customers who are in the target group.
The user may have requirements for other parameters, for example for execution time.
All these constraints are to some extent in conflict, for example accuracy may only be improved by incurring a speed penalty.
Similar considerations apply in other contexts: for example, in an engine management system improved dynamic performance will usually incur a detriment in fuel economy and engine life. There may be rules that have to be applied, for example on engine emissions. In traffic management applications (air, road, rail etc), additional throughput may be obtained only at the expense of speed, punctuality, risk factors etc.
The user of a prior art system will select an appropriate process for the required purpose, in accordance with such requirements. The main objective of picking the right process is finding the right balance, i.e. optimising the match of requirements with properties.
Systems exist that can select a process optimised to a given set of requirements. An example is disclosed in International Patent Application WO03/027899, which discloses a method of selecting a data analysis method in accordance with a user preference, wherein the user preference relates to a feature of the data analysis method and is represented by a fuzzy set comprising a range of values, the method comprising the steps of    (i) using the user preference to identify one or more rules corresponding to the user preference, each rule comprising at least one fuzzy set that relates features of data analysis methods to data analysis characteristics;    (ii) evaluating the or each identified rule, thereby identifying an instance of a data analysis characteristic associated with the identified rule, the instance comprising a fuzzy set having a range of values;    (iii) retrieving data identifying a plurality of data analysis methods, each of which has a plurality of data analysis characteristics, wherein, in respect of each said data analysis characteristic, the retrieved data includes a range of values; and    (iv) comparing the retrieved data with the data analysis characteristic instance in order to identify a data analysis method that matches the user preference.
However, such a system is not suited to situations in which the environment is changing continuously and unpredictably. In customer relations management systems, for instance, customer behaviour, and consequently the data relating to those customers, may respond to changes in the respective market. A process like the one discussed above needs to adapt continuously to such changes, or may even need to be replaced. In additional, mechanical systems such as engines may suffer physical deterioration, or changes in their environment, requiring an update of the controlling process to accommodate these changed characteristics.
Adapting control systems to changing requirements or environments has in the past been tackled in one of three ways. Firstly, a manual redesign of the control software may be performed and installed. Secondly, if the range of possible requirements and environmental changes is known beforehand, a set of possible solutions may be provided in advance, with the user selecting the most appropriate for the present situation. Thirdly, a parameterised process may be used, wherein the parameters adapt to measured changes.
The first approach requires manual intervention resulting in high costs and the risk of introducing new faults. The range of applications to the second approach is heavily restricted since the range of possible changes must be known beforehand. This second approach is not feasible if there are a large variety of possible changes, since it requires a great number of possible solutions to be prepared. The third approach is also impractical unless the variety of different requirements or changes can be predicted well enough to ensure the measured parameters are indicative of the actual changes encountered in the environment.